基于密度图多目标追踪的时空数据可视化

The spatiotemporal data tracking visualization has received widespread attention. The focus of this research is on depicting the dynamic details of the data and ensuring trajectory consistency with the observation results. In this paper, a model that combined deep learning with traditional tracking techniques was proposed to perform tracking tasks, thereby improving the speed and accuracy of visualization. First, a high-quality Perlin noise dataset was generated, on which a multi-target tracking model was trained. Second, a two-stage, multi-model deep learning framework was proposed to enhance the analysis depth of dynamic scenes. Finally, in order to continuously display detailed tracking information, a visualization solution that combined trajectories and vector fields was introduced to enhance the visual effect of tracking information. Different cases in this study demonstrated the usefulness and robustness of the proposed method, quantitatively evaluating and omparing the method from multiple aspects. The results showed that the method proposed in this study can help users in understanding multi-target tracking information in different scenarios.